ROMNet：更新旧的记忆
ROMNet: Renovate the Old Memories
摘要：
修复旧照片中的记忆是计算机视觉领域的一个有趣的研究课题。这些遗留图像通常会遭受严重和混合的退化，例如裂缝，噪声和颜色褪色，而缺乏大规模配对的旧照片数据集使得这项恢复任务非常具有挑战性。在这项工作中，我们提出了一种新的基于参考的端到端学习框架，可以联合修复和着色退化的遗留图片。具体而言，该框架由三个模块组成：用于退化恢复的恢复子网络，用于颜色直方图匹配和传输的相似性子网络，以及学习预测以彩色参考信号为条件的图像的色度元素的着色子网络。整个系统利用了给定参考图像中的颜色直方图先验，这大大降低了对大规模训练数据的依赖。除了所提出的方法，我们还创建了，据我们所知，第一个公共和现实世界的老照片数据集与配对的地面真理评估老照片恢复模型，其中每张老照片是由PhotoShop专家手动恢复原始图像配对。我们在合成和真实世界数据集上进行的大量实验表明，我们的方法在定量和定性方面都显着优于最先进的方法。
英文摘要：
Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cracks, noise, and color-fading, while lack of large-scale paired old photo datasets makes this restoration task very challenging. In this work, we present a novel reference-based end-to-end learning framework that can jointly repair and colorize the degraded legacy pictures. Specifically, the proposed framework consists of three modules: a restoration sub-network for degradation restoration, a similarity sub-network for color histogram matching and transfer, and a colorization subnet that learns to predict the chroma elements of the images conditioned on chromatic reference signals. The whole system takes advantage of the color histogram priors in a given reference image, which vastly reduces the dependency on large-scale training data. Apart from the proposed method, we also create, to our knowledge, the first public and real-world old photo dataset with paired ground truth for evaluating old photo restoration models, wherein each old photo is paired with a manually restored pristine image by PhotoShop experts. Our extensive experiments conducted on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-arts both quantitatively and qualitatively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
